Compressive sensing (CS) is a novel acquisition and processing technique that enables reconstruction of sparse signals from a set of non-adaptive measurements sampled at a much lower rate than required by the Nyquist-Shannon sampling theorem as pertaining to the full signal bandwidth. In particular, compressive sensing exploits the fact that the information bandwidth of the signal is much smaller than the full signal bandwidth.

For radar and ESM sensors, the use of CS may lead to several benefits, such as significant hardware reductions in ESM receivers, unambiguous signal recovery from incomplete measurements (filling in missing data) in interleaved radar modes (SAR/GMTI, multi-function radars) and sparse arrays, high resolution imaging with significantly less data and/or hardware.

The main objective of this Research Lecture Series is to present the fundamentals and the cutting edge of CS techniques for a number of radar and ESM applications, with an outlook to expected future developments and hardware implications of CS based architectures.

Topics to be covered:

Introduction and overview of CS applied to radar.

Introduction and application of CS for Electronic Support Measures (ESM).

Introduction and application of CS to Inverse SAR.

Hardware architectures for compressive sensing.

Application of CS to 2D/3D SAR.

Applications of convex optimization to GMTI.

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